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Hyperspectral imaging (HSI) has emerged as a transformative technology across science and engineering, enabling the capture of rich spectral information for every pixel in an image. From remote sensing and environmental monitoring to precision agriculture and medical diagnostics, the high-dimensional data offered by hyperspectral sensors unlocks insights far beyond conventional imaging. Yet the complexity and volume of hyperspectral datasets present unique challenges for analysis, interpretation, and practical implementation. This book bridges this gap by introducing readers to the fundamental concepts of hyperspectral imaging and empowering them with hands-on techniques using Python , a versatile, open-source language that has become central to modern scientific computing.
Designed for students, researchers, and practitioners alike, Hyperspectral Imaging with Python delivers a clear and practical journey from foundational theory to real-world applications. Throughout the chapters, we blend rigorous explanations with reproducible examples, demonstrating how Python’s rich ecosystem including libraries such as NumPy, scikit-image, and machine learning frameworks, that can be harnessed to explore, visualize, and analyze hyperspectral data. Whether the reader aims to build classification models, perform spectral unmixing, or develop custom processing pipelines, this book serves as both an educational introduction and a practical reference for advancing their work in hyperspectral imaging.
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